Abstract:In recent years, the theory of machine learning and its applications to severe convective weather has been developed at an unprecedented speed. Various machine learning algorithms, such as random forest, decision tree, support vector machine, neural network and deep learning have played important roles in severe convective weather monitoring, nowcasting, short-term forecasting and short-range forecasting. These algorithms often have better performances than traditional methods. With the help of machine learning, it is easier to extract the mesoscale features of convective systems in high spatio-temporal resolution observation data, resulting in better performances of automatic convective weather identification and tracking and warning. Machine learning is also a good tool to effectively use the multi-source observation data, analyze the observation and numerical weather prediction (NWP) data. In addition, machine learning can also be an effective postprocessing method for NWP. It has been showed that machine learning can extract the features of severe weather occurrence from global or regional NWP data and give a reliable severe weather forecasts. Finally, the issues and outlooks of machine learning application are presented.